The non-invasive measurement of substances in the human body by quantitative spectroscopy has been found to be highly desirable, yet very difficult to accomplish. Non-invasive measurements via quantitative spectroscopy are desirable because they are painless, do not require a fluid draw from the body, carry little risk of contamination or infection, do not generate any hazardous waste and have short measurement times. A prime example of a desirable application of such technology is the non-invasive measurement of blood glucose levels in diabetic patients, which would greatly improve diabetes treatment. U.S. Pat. No. 5,379,764 to Bames et al. discloses the necessity for diabetics to frequently monitor blood glucose levels. The more frequent the blood glucose levels are measured, the less likely the occurrence of large swings in blood glucose levels. These large swings are associated with the very undesirable short-term symptoms and long-term complications of diabetes. Such long-term complications include heart disease, arteriosclerosis, blindness, stroke, hypertension, kidney failure and premature death.
Several systems have been proposed for the non-invasive measurement of blood glucose levels. These systems have included technologies incorporating polarimetry, mid-infrared spectroscopy, Raman spectroscopy, Kromoscopy, fluorescence spectroscopy, nuclear magnetic resonance spectroscopy, radio-frequency spectroscopy, ultrasound, transdermal measurements, photoacoustic spectroscopy and near-infrared spectroscopy. However, despite these efforts, direct and invasive measurements (e.g., blood sampling by a lancet cut into the finger) are still necessary for most, if not all, presently FDA approved and commercially available glucose monitors. Because invasive measurements are painful, inconvenient and costly to the diabetic patient, sufficiently frequent blood glucose measurement, which is necessary to ensure effective diabetes management, is rarely achieved.
Of particular interest to the present invention are prior art systems which incorporate or generally utilize quantitative infrared spectroscopy as a theoretical basis for the analysis. In general, these methods involve probing glucose-containing tissue using infrared radiation in absorption or diffuse reflectance mode. It is known that glucose absorbs at multiple frequencies in both the mid- and near-infrared range. There are, however, other infrared active analytes in the tissue and blood that also absorb at similar frequencies. Due to the overlapping nature of these absorption bands, no single or specific frequency can be used for reliable non-invasive glucose measurement. Analysis of spectral data for glucose measurement thus requires evaluation of many intensities over a wide spectral range to achieve the sensitivity, precision, accuracy, and reliability necessary for quantitative determination.
For example, Robinson et al. in U.S. Pat. No. 4,975,581 disclose a method and apparatus for measuring a characteristic of unknown value in a biological sample using infrared spectroscopy in conjunction with a multivariate model that is empirically derived from a set of spectra of biological samples of known characteristic values. The above-mentioned characteristic is generally the concentration of an analyte, such as glucose, but also may be any chemical or physical property of the sample. The method of Robinson et al. involves a two-step process that includes both calibration and prediction steps.
In the calibration step, the infrared light is coupled to calibration samples of known characteristic values so that there is attenuation of at least several wavelengths of the infrared radiation as a function of the various components and analytes comprising the sample with known characteristic value. The infrared light is coupled to the sample by passing the light through the sample or by reflecting the light off the sample. Absorption of the infrared light by the sample causes intensity variations of the light that are a function of the wavelength of the light. The resulting intensity variations at a minimum of several wavelengths are measured for the set of calibration samples of known characteristic values. Original or transformed intensity variations are then empirically related to the known characteristics of the calibration samples using multivariate algorithms to obtain a multivariate calibration model. The model preferably accounts for subject variability, instrument variability and environment variability.
In the prediction step, the infrared light is coupled to a sample of unknown characteristic value, and a multivariate calibration model is applied to the original or transformed intensity variations of the appropriate wavelengths of light measured from this unknown sample. The result of the prediction step is the estimated value of the characteristic of the unknown sample. The disclosure of Robinson et al. is incorporated herein by reference.
A further method of building a calibration model and using such model for prediction of analytes and/or attributes of tissue is disclosed in commonly assigned U.S. Pat. No. 6,157,041 to Thomas et al., entitled “Method and Apparatus for Tailoring Spectrographic Calibration Models,” the disclosure of which is incorporated herein by reference.
In “Near-Infrared Spectroscopy for Non-invasive Monitoring of Metabolites”, Clinical Chemistry Lab Med 2000, 38(2): 137-145, 2000, Heise et al. disclose the non-invasive measurement of glucose in the inner lip of a subject utilizing a Fourier transform infrared (FTIR) spectrometer and a diffuse reflectance accessory. The instrument used for this measurement contained a tungsten light source with an output that was collimated and sent into a Bruker FS-66 FTIR spectrometer. The FTIR spectrometer modulated the light in a manner that created an interferogram and the collimated interferogram was sent to a diffuse reflectance accessory. The diffuse reflectance accessory was a bifurcated, Y-shaped fiber optic probe. The input fibers of the probe radiated the inner lip of a subject or a spectralon reference standard with the interferogram from the FTIR spectrometer. Light diffusely reflected from the inner lip was collected by the output fibers of the diffuse reflectance accessory and focused onto a liquid nitrogen cooled InSb detector. The optical interferograms were converted to an electrical signal by the InSb detector and the electrical signal was digitized by an analog-to-digital converter (ADC). The digitized interferogram was then converted into an NIR spectrum and a collection of these spectra and corresponding blood glucose reference values were correlated using multivariate techniques to produce a calibration for non-invasive glucose measurements. This instrument was able to produce cross-validated, leave-one-out-at-a-time glucose standard error of predictions (SEP) of 36.4 mg/dl. This level of accuracy and precision is not of clinical utility.
In “Near-Infrared Spectrometric Investigation of Pulsatile Blood Flow for Non-Invasive Metabolite Monitoring”, CP430, Fourier Transform Spectroscopy: 11th International Conference, 1998, Heise et al. discuss the non-invasive measurement of glucose in the inner lip of a subject using multivariate analysis of spectra with pulsatile blood flow. Heise et al. assert that by taking the difference between the systolic and diastolic portions of the cardiac cycle, interferences can be removed and glucose predictions are done on the spectra due to the additional blood volume. The optical pathlength due to the additional blood volume is 50 to 70 times shorter than an integrated NIR measurement, resulting in a dramatically reduced glucose signal-to-noise ratio (SNR). Heise used the instrument described in the preceding paragraph to make his measurements. No glucose prediction results were disclosed.
In “Spectroscopic and Clinical Aspects of Non-invasive Glucose Measurements”, Clinical Chemistry, 45:2, 165-177, 1999, Khalil gives an overview of non-invasive glucose monitoring techniques. Khalil covers NIR transmission and reflectance, mechanical manipulation of the tissue coupled with NIR spectroscopy, Kromoscopy, spatially resolved diffuse reflectance, frequency domain measurements, polarimetry measurements, Raman spectroscopy and photo-acoustic methods.
In U.S. Pat. No. 5,361,758, Hall et al. describe a method and apparatus for the non-invasive measurement of glucose. This device is composed of a broadband light source, transfer optics from the light source to the sampling accessory, a tissue sampling accessory, transfer optics from the tissue sampling accessory to a dispersive spectrometer whose main optical elements are a diffraction grating and a linear array detector and finally processing and display subsystems. Hall et al. disclose taking the second derivative of the NIR absorbance spectrum collected by the above instrument and applying a calibration model to the second derivative of the absorbance spectrum to predict glucose concentrations.
In U.S. Pat. No. 5,743,262, Lepper, Jr. et al. describe a method and apparatus for the non-invasive measurement of glucose. This device is composed of a broadband light source, a collimating optic for the light source, an optical filter for modulating the output of the light source, a tissue sampling accessory, a photodetector, a data acquisition subsystem and a signal processing subsystem. The optical filter passes select wavelengths of light from the broadband source in a given time interval. The selected wavelength of light is sent into the tissue-sampling accessory to irradiate the tissue. Light collected from the tissue is focused onto a detector, and the electrical signal output from the detector is digitized by an analog-to-digital converter. The signal processing subsystem takes a “double log” transformation of the signal and then uses the result to predict glucose concentrations.
In U.S. Pat. No. 5,750,994, Schlager describes a method and apparatus for non-invasive measurement of glucose in the NIR range using optical transfer cells that have positive correlation filters that are selective for the analyte of interest. This apparatus includes a dispersive spectrometer along with a broadband light source, a tissue-sampling accessory, a detector or linear array detector and a data acquisition subsystem.
In U.S. Pat. No. 5,830,112, Robinson describes a general method of robust sampling of tissue for non-invasive analyte measurement. The sampling method utilizes a tissue-sampling accessory that is pathlength optimized by spectral region for measuring an analyte such as glucose. The patent discloses several types of spectrometers for measuring the spectrum of the tissue from 400 to 2500 nm, including acousto-optical tunable filters, discrete wavelength spectrometers, filters, grating spectrometers and FTIR spectrometers. The disclosure of Robinson is incorporated hereby reference.
In U.S. Pat. No. 6,016,435, Maruo et al. describe an apparatus for the non-invasive measurement of glucose. This device uses a broadband light source coupled to a stepped grating monochrometer to generate successive wavelengths of light in the NIR spectral region. The output of the monochrometer is sent to an optical fiber bundle that samples the tissue of a subject. The optical fiber bundle radiates the skin with the light from the monochrometer and collects diffusely reflected light from the skin of the subject. The collected diffuse reflectance spectrum is sent to a detector and the electrical signal from the detector is digitized. An absorbance spectrum is generated from the digitized output of the detector and that diffuse reflectance spectrum is used to make a prediction of glucose concentration.
In U.S. Pat. No. 6,026,314, Amerov et al. describe a method and apparatus for the non-invasive measurement of glucose that utilizes pulsed, discrete wavelengths of light in the NIR spectral region. The pulsed light source may be a flash lamp, light emitting diodes or laser diodes. The output of the pulsed light source is coupled to a tissue-sampling accessory that utilizes prisms or fiber optics to irradiate the tissue and collect absorbance spectra from the tissue. The output of the sampling accessory is sent to one or more detectors which convert the optical signal to an electrical signal. The electrical signals from the detectors are amplified and undergo analog-to-digital conversion. The digitized signals are then processed, and an algorithm is applied to predict glucose concentration.
In U.S. Pat. No. 6,049,727, Crothall describes an implanted glucose sensing system that measures glucose in vivo and is meant to couple to an insulin pump to create an artificial pancreas. The implanted sensor uses a number of discrete wavelengths which irradiate a blood vessel. The light is absorbed and scattered by the blood and tissue in the optical path between the light sources and the detector. The detected light is converted from an optical signal to an electrical signal, and then digitized by an analog-to-digital converter. The digitized signal is sent to a radio frequency transceiver which communicates with an external processing system to apply an algorithm to the digitized absorbance spectrum to calculate glucose concentration. The resulting glucose concentration information is utilized to control the administration of insulin to the subject by an insulin pump. This closed loop system is meant to create an artificial pancreas for insulin dependent diabetics.
In U.S. Pat. No. 6,061,582, Small et al. describe a method and apparatus for non-invasive determination of glucose. The apparatus for the measurement includes a broadband light source, an FTIR spectrometer, tissue sampling accessory, a detector and data acquisition system and a processing system. The spectra collected from the subject are digitally filtered to isolate a portion of the spectrum due to the glucose signal. Multivariate analysis techniques are then applied to the digitally filtered spectrum to generate a glucose prediction. The tissue-sampling accessory can collect spectra from the subject using transmission or diffuse reflectance.
In PCT Application, WO 99/43255, Small et al. describe a non-invasive glucose monitoring apparatus and method that measures glucose by transmission of NIR light through the tongue of a subject. The apparatus for the measurement includes a broadband light source, an FTIR spectrometer, tissue sampling accessory, a detector and data acquisition system and a processing system. The prediction results presented in this application do not achieve the levels of precision and accuracy necessary for clinical application.
In “New Approach to High-Precision Fourier Transform Spectrometer Design”, Applied Optics, 35:16, 2891-2895, 1996, Brault introduces a constant time sampling analog-to-digital conversion technique for FTIR spectrometers that allows use of high dynamic range delta-sigma ADCs. Brault asserts their approach provides a superior technique for implementing the data acquisition system of an FTIR spectrometer because it avoids the artifacts of gain ranging and the need to precisely match the time delays between the laser reference and infrared measurement channels. In “Uniform Time-Sampling Fourier Transform Spectroscopy”, Applied Optics, 36:1-, 2206-2210, 1997, Brasunas et al. discuss a variation of Brault's constant time sampling analog-to-digital conversion technique for FTIR spectrometers.
In U.S. Pat. No. 5,914,780, Turner et al. describe a method of digitizing the interferogram of an FTIR spectrometer using a constant time sampling analog-to-digital converter. The constant time sampling technique allows the use of high dynamic range, delta-sigma analog-to-digital converters that obviate the need for gain ranging circuitry and precisely matched delays between the reference laser and infrared signals. This type of data acquisition system is asserted to provide the FTIR spectrometer with higher SNR and superior photometric accuracy when compared to the previously employed sampling technique which is triggered by the zero crossings of the reference laser.
Although there has been substantial work conducted in attempting to produce a commercially viable non-invasive near-infrared spectroscopy-based glucose monitor, no such device is presently available. It is believed that prior art systems discussed above have failed for one or more reasons to fully meet the challenges imposed by the spectral characteristics of tissue which make the design of a non-invasive measurement system a formidable task. Thus, there is a substantial need for a commercially viable device which incorporates subsystems and methods with sufficient accuracy and precision to make clinically relevant measurements of analytes, such as glucose, in human tissue.